Machine learning (ML) is disrupting businesses across the world, creating waves within the IT community and fueling total organisational transformations. There’s no doubt that organisations everywhere (probably even yours) are jumping on the bandwagon and working to implement ML capabilities. But have you asked yourself the question, “How successfully is my organisation applying machine-generated insight?”
What would your honest response be?
My prediction is that all too often these insights are uncovered but not applied because business leaders neither understand nor trust the outcomes, or may not have the budget or the skillset needed to make real changes. For others, ML has become a check box—you buy it, you check it off the list, and you never touch it again.
>See also: What is machine learning?
If your organisation has or is considered purchasing ML capabilities, you need to first understand what ML learning actually is, the role big data plays in delivering insight, and what actions you should be taking now to truly transform with ML.
Machine learning, in a nutshell
Do you ever hear people refer to artificial intelligence (AI) and ML as one in the same? The first step in executing a successful ML strategy is to understand that it’s different from AI. Artificial intelligence is technology designed to perform tasks that are typically reserved for humans (for example, voice assistants like Alexa or Siri).
Machine learning falls within AI, but uses algorithms to generate learnings from structured and unstructured data. It sifts through large data sets which might include images, text, voice, video, location and even facial recognition data. By analysing these data sets, ML identifies correlations, patterns and trends that can be used to make predictions.
The role of big data
Machine learning is unique in that it works very much like the human brain does. The more information you input, the smarter it becomes. Globally, businesses and consumers collectively produce 2.5 quintillion bytes of data each day, which is enough to fill 100 million blu-ray discs! For ML, this amount of data would be considered a feast because it thrives on large sets of unstructured and structured data, which can reveal hidden forecasts, predictions and insights using algorithms.
You’ve likely experienced the end result of this analytical process for yourself without even realizing it. If you like to Netflix (and chill), you’ve seen a category for “Because you recently watched…” which serves up recommended shows based on your past viewing behaviors. Or if you post photos on Facebook you’ve probably used the facial recognition capabilities to tag a photo instead of typing in the person’s name. And following Amazon Prime Day, you may still receive product suggestions that complement your discount purchases.
Delivering more than just lip service
Companies like Netflix, Facebook and Amazon are great examples of how to capitalise on big data and ML capabilities to deliver superior customer experiences. Every organisation has the ability to capture and analyse big data, but it’s how to turn that insight into action that ultimately counts.
Think about it this way—you can buy a gym membership, but if you don’t actually go to the gym and use the machines, you’ll never get the desired outcome. Unfortunately, this isn’t the mentality of most organisations, and many purchase ML software but don’t put in the extra effort for it to drive any real business value.
Barriers like culture, budget constraints, internal talent, or just a lack of desire to change the status quo have plagued organisations and prevented them from transitioning from “early adopters” to “innovators.”
Face your fears
Despite these organisational challenges, business leaders have an opportunity to face their fears, and take immediate actions to help remove barriers and move the needle with ML strategies:
1. Integrate ML into your digital transformation journey. By doing this during the planning stage, you can prevent ML from being left behind. Employees will include ML from the start and treat it with equal importance.
2. Make a commitment. And make it public. By letting others know you’re committed to understanding, embracing, adopting and integrating ML, it holds you accountable.
3. Start at the top. You cannot be the only one aboard the ML ship—you need your leadership team right there with you. Encourage leaders to incorporate analytics into the strategic vision to foster an analytics culture.
4. Assess internal talent. Identify people in the organisation who can be ambassadors for ML. And don’t be afraid to call out where there are gaps. Invest in outside talent that can bring these skills into your organisation.
5. Eat your own lunch. Machine learning capabilities shouldn’t just be for the benefit of the customer. Implement these technologies within your own organisation so that employees can see the value they generate first-hand.
6. Stay focused. Don’t purchase ML capabilities just to say you have it. Know your desired outcome and design your strategy to meet those specific needs. Through this process, you’ll gradually uncover other areas that the technologies can benefit, ensuring you’re always putting resources where they count.
7. Ask for help. Machine learning isn’t an easy concept. Ask the tough questions and make sure that everything you do ultimately benefits someone, whether it’s your customer or your employee.
8. Start small. Roll out small-scale projects initially so that you can test the waters with low risk. This will give you the chance to familiarise yourself better with the technology so that you’re successful with larger projects.
Everyone can all probably agree that ML is one of the biggest technology change agents in existence. But reaping the full benefits of it cannot come from the technology alone. Business leaders have an immediate opportunity to capitalise on these capabilities and foster a transformation marked by innovation. Don’t just check the box. Be a change agent.
Sourced by Greg Van den Heuvel, chief operations officer, Pitney Bowes Software